METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM)
UNTUK ANALISIS SENTIMEN APLIKASI BING: CHAT WITH
AI & GPT-4 DI GOOGLE PLAY STORE
Muslih; Atiqah Meutia Hilda;
No. Panggil: eSTI-2003015144
Metode Klasifikasi Support Vector Machine (SVM) untuk Analisis Sentimen
Aplikasi Bing: Chat with AI & GPT-4 di Google Play Store
Muslih
Perkembangan Artificial Intelligence (AI), terutama melalui teknologi ChatGPT dan munculnya
Bing: Chat with AI & GPT-4, menciptakan landasan untuk penelitian ini. Pada studi ini dilakukan
analisis sentimen terhadap pengguna aplikasi Bing: Chat with AI & GPT-4 menggunakan metode
klasifikasi Support Vector Machine (SVM). Sebanyak 10.351 ulasan Google Play Store melalui
tahapan preprocessing yang meliputi pembersihan, casefolding, tokenizing, stop word removal,
stemming dan filtering. Penerapan metode pembobotan TF-IDF, dan klasifikasi Support Vector
Machine (SVM) menghasilkan sentimen negatif sebesar 58,37% dan sentimen positif sebesar
41,66%, dengan akurasi pengujian sebesar 89,44%. Visualisasi worldcloud menggunakan kata-kata
umum dalam ulasan positif dan negatif, memberikan wawasan tentang preferensi dan ketidakpuasan
pengguna, terutama mengenai kecepatan dan kinerja Bing: Chat with AI & GPT-4. Studi ini
menyimpulkan bahwa peningkatan kualitas aplikasi sangat penting untuk kepuasan pengguna di
masa depan, dan rekomendasi penelitian di masa depan mencakup penggunaan data dari platform
lain seperti App Store dan media sosial untuk analisis sentimen yang komprehensif.
Kata kunci: AI, Bing: Chat with AI & GPT-4, Analisis Sentimen, Support Vector Machine, Google
Play Store
Support Vector Machine (SVM) Classification Method for Bing App Sentiment
Analysis: Chat with AI & GPT-4 on Google Play Store
Muslih
The development of Artificial Intelligence (AI), particularly through ChatGPT technology and the
emergence of Bing: Chat with AI & GPT-4, forms the foundation for this research. This study
conducts sentiment analysis on users of the Bing: Chat with AI & GPT-4 application using the
Support Vector Machine (SVM) classification method. A total of 10,351 reviews from the Google
Play Store underwent preprocessing stages, including cleaning, case-folding, tokenizing, stop word
removal, stemming, and filtering. The implementation of the TF-IDF weighting method and Support
Vector Machine (SVM) classification resulted in a negative sentiment of 58.37% and a positive
sentiment of 41.66%, with a testing accuracy of 89.44%. Visualization using word clouds,
incorporating common words in positive and negative reviews, provides insights into user
preferences and dissatisfaction, particularly regarding the speed and performance of Bing: Chat
with AI & GPT-4. The study concludes that improving the application's quality is crucial for future
user satisfaction, and future research recommendations include utilizing data from other platforms
such as the App Store and social media for comprehensive sentiment analysis.
Keywords: AI, Bing: Chat with AI & GPT-4, Sentiment Analysis, Support Vector Machine, Google
Play Store
